Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language ...(BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/.
The commemorative publication in honor of Prof. Bernhard Heck contains 41 contributions written by friends, colleagues, co-workers and former PhD-students. The focus of the scientific articles is on ...Physical and Satellite Geodesy as well as on geodynamics. It reflects the wide range of academic and scientific research activities of Bernhard Heck. The volume is completed by articles about education at the Geodetic Institute of KIT as well as the importance of Geodesy for society.
With this study, we provide a comprehensive reference dataset of detailed miRNA expression profiles from seven types of human peripheral blood cells (NK cells, B lymphocytes, cytotoxic T lymphocytes, ...T helper cells, monocytes, neutrophils and erythrocytes), serum, exosomes and whole blood. The peripheral blood cells from buffy coats were typed and sorted using FACS/MACS. The overall dataset was generated from 450 small RNA libraries using high-throughput sequencing. By employing a comprehensive bioinformatics and statistical analysis, we show that 3' trimming modifications as well as composition of 3' added non-templated nucleotides are distributed in a lineage-specific manner-the closer the hematopoietic progenitors are, the higher their similarities in sequence variation of the 3' end. Furthermore, we define the blood cell-specific miRNA and isomiR expression patterns and identify novel cell type specific miRNA candidates. The study provides the most comprehensive contribution to date towards a complete miRNA catalogue of human peripheral blood, which can be used as a reference for future studies. The dataset has been deposited in GEO and also can be explored interactively following this link: http://134.245.63.235/ikmb-tools/bloodmiRs.
Alzheimer's disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high ...drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.
miRNA plays an important role in tumourgenesis by regulating expression of oncogenes and tumour suppressors. Thus affects cell proliferation and differentiation, apoptosis, invasion and angiogenesis. ...miRNAs are potential biomarkers for diagnosis, prognosis and therapies of different forms of cancer. However, relationship between response of cancer patients towards targeted therapy and the resulting modifications of the miRNA transcriptome in the context of pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have produced an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. Efficient translation of -omics data and accumulated knowledge to clinical decision-making are of paramount scientific and public health interest. Well-structured translational algorithms are needed to bridge the gap from databases to decisions. Herein, we present a novel SMARTmiR algorithm to prospectively predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer.
Neurodegenerative diseases are incurable and debilitating indications with huge social and economic impact, where much is still to be learnt about the underlying molecular events. Mechanistic disease ...models could offer a knowledge framework to help decipher the complex interactions that occur at molecular and cellular levels. This motivates the need for the development of an approach integrating highly curated and heterogeneous data into a disease model of different regulatory data layers. Although several disease models exist, they often do not consider the quality of underlying data. Moreover, even with the current advancements in semantic web technology, we still do not have cure for complex diseases like Alzheimer's disease. One of the key reasons accountable for this could be the increasing gap between generated data and the derived knowledge.
In this paper, we describe an approach, called as NeuroRDF, to develop an integrative framework for modeling curated knowledge in the area of complex neurodegenerative diseases. The core of this strategy lies in the usage of well curated and context specific data for integration into one single semantic web-based framework, RDF. This increases the probability of the derived knowledge to be novel and reliable in a specific disease context. This infrastructure integrates highly curated data from databases (Bind, IntAct, etc.), literature (PubMed), and gene expression resources (such as GEO and ArrayExpress). We illustrate the effectiveness of our approach by asking real-world biomedical questions that link these resources to prioritize the plausible biomarker candidates. Among the 13 prioritized candidate genes, we identified MIF to be a potential emerging candidate due to its role as a pro-inflammatory cytokine. We additionally report on the effort and challenges faced during generation of such an indication-specific knowledge base comprising of curated and quality-controlled data.
Although many alternative approaches have been proposed and practiced for modeling diseases, the semantic web technology is a flexible and well established solution for harmonized aggregation. The benefit of this work, to use high quality and context specific data, becomes apparent in speculating previously unattended biomarker candidates around a well-known mechanism, further leveraged for experimental investigations.
Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues ...to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article's supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer's disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html.
Policy decisions in governmental models are often based on their perception and acceptance in the general public. Traditional methods for harvesting opinions like telephone or street surveys are time ...intensive and costly and direct interaction between a governmental member and the population is limited. Social media harbor the chance to easily get a high number of opinions and proposals in form of poll participation or interactive debate contributions.
Especially debates about political topics can generate data which are hard to interpret because of its length and complexity. We propose a collection of methods to support a decision maker in gaining an overview over textual debates coming from several social media to save time and effort in manual analysis. Our approach enables an efficient decision making process by a combination of automatic topic clustering, sentiment analysis, filtering, and search functionalities aggregated in a graphical user interface. We present an implementation and a use case proving the usefulness of the proposed methodologies.
This paper introduces an overview of an automated methodology for electrical black box behavioural modelling. The presented approach rests upon the transient simulation data of the in and output pins ...of an analogue or mixed signal circuit. This method extracts the magnitude and the phase information by measuring in- and output currents and voltages and creates a new electrical pin compatible black-box model which approximates the real electric behaviour of the original circuit. The behavioural model is made up of cross connected admittance models whose connections consist of complex functions which are approximated using Support Vector Machines. The electrical behavioural models have a speed-up factor up to 37x and an accuracy greater than 95% compared to the original circuit simulation. A special filter structure enables the modeling of frequency, amplitude and load dependent circuits. The electrical functioning of the methodology is shown by a passive second order Butterworth filter and a time variant and active charge pump circuit out of the automotive industry.
Abstract
BACKGROUND
Navigated transcranial magnetic stimulation (nTMS) is an established, noninvasive tool to preoperatively map the motor cortex. Despite encouraging reports from few academic ...centers with vast nTMS experience, its value for motor-eloquent brain surgery still requires further exploration.
OBJECTIVE
To further elucidate the role of preoperative nTMS in motor-eloquent brain surgery.
METHODS
Patients who underwent surgery for a motor-eloquent supratentorial glioma or metastasis guided by preoperative nTMS were retrospectively reviewed. The nTMS group (n = 105) was pair-matched to controls (non-nTMS group, n = 105). Gross total resection (GTR) and motor outcome were evaluated. Subgroup analyses including survival analysis for WHO III/IV glioma were performed.
RESULTS
GTR was significantly more frequently achieved in the entire nTMS group compared to the non-nTMS group (P = .02). Motor outcome did not differ (P = .344). Bootstrap analysis confirmed these findings. In the metastases subgroup, GTR rates and motor outcomes were equal. In the WHO III/IV glioma subgroup, however, GTR was achieved more frequently in the nTMS group (72.3%) compared to non-nTMS group (53.2%) (P = .049), whereas motor outcomes did not differ (P = .521). In multivariable Cox-regression analysis, prolonged survival in WHO III/IV glioma was significantly associated with achievement of GTR and younger patient age but not nTMS mapping.
CONCLUSION
Preoperative nTMS improves GTR rates without jeopardizing neurological function. In WHO III/IV glioma surgery, nTMS increases GTR rates that might translate into a beneficial overall survival. The value of nTMS in the setting of a potential survival benefit remains to be determined.
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